BayesSurvive: Bayesian Survival Models for High-Dimensional Data

An implementation of Bayesian survival models with graph-structured selection priors for sparse identification of omics features predictive of survival (Madjar et al., 2021 <doi:10.1186/s12859-021-04483-z>) and its extension to use a fixed graph via a Markov Random Field (MRF) prior for capturing known structure of omics features, e.g. disease-specific pathways from the Kyoto Encyclopedia of Genes and Genomes database.

Version:	0.0.2
Depends:	R (≥ 4.0)
Imports:	Rcpp, ggplot2, GGally, mvtnorm, survival, riskRegression, utils, stats
LinkingTo:	Rcpp, RcppArmadillo (≥ 0.9.000)
Suggests:	knitr
Published:	2024-06-04
DOI:	10.32614/CRAN.package.BayesSurvive
Author:	Zhi Zhao [aut, cre], Katrin Madjar [aut], Tobias Østmo Hermansen [aut], Manuela Zucknick [ctb], Jörg Rahnenführer [ctb]
Maintainer:	Zhi Zhao <zhi.zhao at medisin.uio.no>
BugReports:	https://github.com/ocbe-uio/BayesSurvive/issues
License:	GPL-3
URL:	https://github.com/ocbe-uio/BayesSurvive
NeedsCompilation:	yes
Materials:	README NEWS
CRAN checks:	BayesSurvive results

Documentation:

Reference manual:	BayesSurvive.pdf
Vignettes:	Bayesian Cox Models with graph-structure priors

Downloads:

Package source:	BayesSurvive_0.0.2.tar.gz
Windows binaries:	r-devel: BayesSurvive_0.0.2.zip, r-release: BayesSurvive_0.0.2.zip, r-oldrel: BayesSurvive_0.0.2.zip
macOS binaries:	r-release (arm64): BayesSurvive_0.0.2.tgz, r-oldrel (arm64): BayesSurvive_0.0.2.tgz, r-release (x86_64): BayesSurvive_0.0.2.tgz, r-oldrel (x86_64): BayesSurvive_0.0.2.tgz
Old sources:	BayesSurvive archive

Linking:

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